Abstract:As the direction of the automobile industry, autonomous driving holds a crucial position, particularly in autonomous navigation. The paper investigates autonomous navigation technology which use a sliding window of factor graph optimization based on IMU/LIDAR/GNSS multi-source navigation information fusion. The front end integrates an inertia measurement unit (IMU) and Light Detection and Ranging (LIDAR) to output vehicle attitude and local point nephogram. Based on IMU pre-integration and LIDAR odometer constraints, the algorithm back-end adds Global Navigation Satellite System (GNSS) factors and loop detection factors. The sliding window of the factor graph optimization algorithm is used for multi-source navigation information fusion, which effectively corrects the cumulative error of the front-end and improve the accuracy and robustness of the algorithm. The results of the car experiment show that the algorithm of multi-source navigation information fusion which is based on the sliding window of the factor graph is stable and unaffected by sensor failures when the navigation system encounters sensor failures, and the method is significantly enhanced the scalability of the navigation system.